Predictive models are central to many scientiﬁc disciplines and vital for informingmanagement in a rapidly changing world. However, limited understanding of theaccuracy and precision of models transferred to novel conditions (their ‘trans-ferability’) undermines conﬁdence in their predictions. Here, 50 experts identiﬁedpriority knowledge gaps which, if ﬁlled, will most improve model transfers. Theseare summarized into six technical and six fundamental challenges, which underliethe combined need to intensify research on the determinants of ecologicalpredictability, including species traits and data quality, and develop best prac-tices for transferring models. Of high importance is the identiﬁcation of a widelyapplicable set of transferability metrics, with appropriate tools to quantify thesources and impacts of prediction uncertainty under novel conditions.